QUAGOL: a guide for qualitative data analysis

Int J Nurs Stud. 2012 Mar;49(3):360-71. doi: 10.1016/j.ijnurstu.2011.09.012. Epub 2011 Oct 11.


Background: Data analysis is a complex and contested part of the qualitative research process, which has received limited theoretical attention. Researchers are often in need of useful instructions or guidelines on how to analyze the mass of qualitative data, but face the lack of clear guidance for using particular analytic methods.

Objectives: The aim of this paper is to propose and discuss the Qualitative Analysis Guide of Leuven (QUAGOL), a guide that was developed in order to be able to truly capture the rich insights of qualitative interview data.

Method: The article describes six major problems researchers are often struggling with during the process of qualitative data analysis. Consequently, the QUAGOL is proposed as a guide to facilitate the process of analysis. Challenges emerged and lessons learned from own extensive experiences with qualitative data analysis within the Grounded Theory Approach, as well as from those of other researchers (as described in the literature), were discussed and recommendations were presented. Strengths and pitfalls of the proposed method were discussed in detail.

Results: The Qualitative Analysis Guide of Leuven (QUAGOL) offers a comprehensive method to guide the process of qualitative data analysis. The process consists of two parts, each consisting of five stages. The method is systematic but not rigid. It is characterized by iterative processes of digging deeper, constantly moving between the various stages of the process. As such, it aims to stimulate the researcher's intuition and creativity as optimal as possible.

Conclusion: The QUAGOL guide is a theory and practice-based guide that supports and facilitates the process of analysis of qualitative interview data. Although the method can facilitate the process of analysis, it cannot guarantee automatic quality. The skills of the researcher and the quality of the research team remain the most crucial components of a successful process of analysis. Additionally, the importance of constantly moving between the various stages throughout the research process cannot be overstated.

MeSH terms

  • Data Interpretation, Statistical*